the impact of the non-essential business closure …non-essential counterparts, have a 55% higher...
TRANSCRIPT
NBER WORKING PAPER SERIES
THE IMPACT OF THE NON-ESSENTIAL BUSINESS CLOSURE POLICY ON COVID-19 INFECTION RATES
Hummy SongRyan M. McKenna
Angela T. ChenGuy David
Aaron Smith-McLallen
Working Paper 28374http://www.nber.org/papers/w28374
NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue
Cambridge, MA 02138January 2021
We thank the analytics team at Independence Blue Cross for their support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
© 2021 by Hummy Song, Ryan M. McKenna, Angela T. Chen, Guy David, and Aaron Smith-McLallen. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.
The Impact of the Non-essential Business Closure Policy on Covid-19 Infection RatesHummy Song, Ryan M. McKenna, Angela T. Chen, Guy David, and Aaron Smith-McLallenNBER Working Paper No. 28374January 2021JEL No. H75,I12,I14,I18,J21,J68
ABSTRACT
In response to the Covid-19 pandemic, many localities instituted non-essential business closure orders, keeping individuals categorized as essential workers at the frontlines while sending their non-essential counterparts home. We examine the extent to which being designated as an essential or non-essential worker impacts one’s risk of being Covid-positive following the non-essential business closure order in Pennsylvania. We also assess the intrahousehold transmission risk experienced by their cohabiting family members and roommates. Using a difference-in-differences framework, we estimate that workers designated as essential have a 55% higher likelihood of being positive for Covid-19 than those classified as non-essential; in other words, non-essential workers experience a protective effect. While members of the health care and social assistance subsector contribute significantly to this overall effect, it is not completely driven by them. We also find evidence of intrahousehold transmission that differs in intensity by essential status. Dependents cohabiting with an essential worker have a 17% higher likelihood of being Covid-positive compared to those cohabiting with a non-essential worker. Roommates cohabiting with an essential worker experience a 38% increase in likelihood of being Covid-positive. Analysis of households with a Covid-positive member suggests that intrahousehold transmission is an important mechanism
Hummy SongThe Wharton School University of Pennsylvania 3730 Walnut Street560 Jon M. Huntsman Hall Philadelphia, PA 19104 [email protected]
Ryan M. McKennaIndependence Blue Cross [email protected]
Angela T. ChenThe Wharton School University of Pennsylvania Philadelphia, PA 19104 [email protected]
Guy DavidThe Wharton SchoolUniversity of Pennsylvania305 Colonial Penn Center3641 Locust WalkPhiladelphia, PA 19104-6218and [email protected]
Aaron Smith-McLallenIndependence Blue Cross1900 Market StreetPhiladelphia, PA [email protected]
3
1. Introduction
Reopening the economy during the Covid-19 pandemic involves striking a delicate balance under massive
uncertainty. Policymakers face tremendous pressure to balance concerns about harm from the Covid-19
pandemic with concerns about the negative impact of the lockdown on the welfare and livelihoods of
people. With over 52 million Americans filing claims for unemployment insurance from March 14 to
August 8, 2020 (United States Department of Labor 2020), and over 15 million additional Americans
filing for Pandemic Unemployment Assistance (assistance available to informal sector workers such as
those who are self-employed, seeking part-time employment, or who otherwise would not qualify for
regular unemployment compensation) as of August 22, 2020 (United States Department of Labor 2020),
the Covid-19 pandemic the has led to an unprecedented level of unemployment in the United States
rivaling only the Great Depression.
Globally, 93% of the world’s workers reside in countries with some sort of business closure
measure in place since the start of 2020 (International Labour Organization 2020), with many jobs
shifting into the home. Jobs that have not shifted into the home environment during the pandemic are, for
the most part, jobs designated as “essential.” Workers of these essential jobs have helped society maintain
a semblance of normalcy. The most obvious in this group are health care workers, but employees working
in grocery stores, delivery services, factories and farms, restaurants, transportation, and other industries
are also considered essential workers. Individually, these workers face the same tradeoff confronted by
society at large: on the one hand, essential workers take on greater risk of Covid-19 infection to
themselves and their families, but on the other hand, they maintain financial viability. Conversely, non-
essential workers and their families may be less at risk of Covid-19 infection, but these workers may be
more likely to become unemployed or underemployed.
At the societal level, closure of non-essential businesses has been shown to be effective in
reducing Covid-19 mortality (Ciminelli and Garcia-Mandico 2020), but a lockdown of all non-essential
workers is unlikely to be cost-effective for an extended period (Fischer 2020). Prior work has already
shown that closure of non-essential businesses jeopardized almost a quarter of jobs in the U.S. and
4
reduced total wage income (del Rio-Chanona et al. 2020), had a perverse effect on wage inequality
(Schiavone 2020), and increased unemployment mostly among minorities (Fairlie, Couch, and Xu 2020).
Furthermore, designating businesses as essential or non-essential shifted consumer activity to favor those
categorized as essential. For example, during stay-at-home orders, consumption patterns shifted from
restaurants and bars toward groceries and other food vendors, while maintaining a small impact on
aggregate levels of activity (Goolsbee and Syverson 2020). Similarly, essential retail – the “frontline” job
most in demand during the pandemic – took a much smaller hit in job vacancies, while leisure and
hospitality services and non-essential retail saw the biggest collapses (Forsythe et al. 2020).
Importantly, however, essential workers may be at higher risk of exposure to Covid-19 infection
(Mutambudzi et al. 2020) and also be at greater risk of infecting others. Individuals most susceptible to
infection by essential workers are likely to be those living under the same roof – such as cohabiting
family members. Several epidemiologic studies from China have confirmed that intrahousehold
transmission is a major route by which children become infected with the virus (Cai et al. 2020, Liu et al.
2020, Tan et al. 2020). One early study found that more than half of all patients with Covid-19 had at
least one family member with the disease, and 75 to 80% of all clustered infections were within families
(Chen et al. 2020). Additional work found that household transmission accounted for 30% to 55% of
Covid-19 positive cases after social distancing was implemented (Curmei et al. 2020). These findings
suggest that intrahousehold transmission may be an effective target for policy interventions.
In this paper, we examine the extent to which designation as an essential worker versus a non-
essential worker impacts one’s risk of being positive for Covid-19 following the statewide non-essential
business closure order in Pennsylvania. We also assess the intrahousehold transmission risk experienced
by cohabiting family members and roommates of essential workers versus non-essential workers. This is
a unique analytic problem, as it requires us to link Covid-19 status, employment sector, and physical
address to establish cohabitation status, all at the individual level. Notably, it requires data from not only
individuals testing positive for Covid-19 but also those not tested or testing negative. Data from health
systems and government agencies lack the level of granularity and visibility into those individuals who
5
have not tested positive, thus making it impossible to assess the impact of non-essential business closures
on rates of Covid-19 infection among those working in essential versus non-essential businesses and
those who live in proximity to them. Using data collected by Independence Blue Cross (Independence; a
large commercial health insurer based in southeast Pennsylvania), we are able to construct a uniquely
detailed dataset that merges individual-level Covid-19 status using medical insurance claims, enrollment
and demographic information, and employer industry using North American Industry Classification
(NAICS) codes, which were used to identify a individuals as essential or non-essential workers. This
dataset is also unique in its breadth. Independence provides medical insurance to more than 50% of
commercially insured individuals in the Greater Philadelphia area. The analyses presented here represent
a substantial portion of essential and non-essential workers in the fifth largest metropolitan area in the
United States.
We use a difference-in-differences framework to estimate that essential workers, relative to their
non-essential counterparts, have a 55% higher likelihood of being positive for Covid-19. Said differently,
non-essential workers experience a substantially lower risk of being positive for Covid-19 compared to
their essential counterparts. While members of the health care and social assistance subsector contribute
significantly to this overall effect, it is not completely driven by them. We also find evidence of
intrahousehold transmission that differs in intensity by essential status.
2. Background
On March 19, 2020, less than two weeks after declaring a state of emergency for the Commonwealth of
Pennsylvania, Governor Tom Wolf and Secretary of Health Rachel Levine issued a statewide order for all
non-life-sustaining businesses in Pennsylvania to close their physical locations (Governor Tom Wolf
2020a). These orders were enforced by law; consequences of failed compliance with the order included
citations, fines, or license suspensions. Accompanying the order, Governor Wolf included a list of life-
sustaining businesses that were permitted to continue physical operations, identified using NAICS codes.
In this paper, we refer to those employed by businesses that were permitted to continue operations (such
6
as hospitals, transportation systems, and food manufacturing) as essential workers; those employed by
businesses that were forced to temporarily close (including mining activities, construction, and general
merchandise stores) are considered non-essential workers.
The criteria for classifying businesses and their employees into essential and non-essential
categories were somewhat arbitrary. Although the Department of Homeland Security’s Cybersecurity and
Infrastructure Security Agency (CISA) published guidance for identifying essential industries
(Cybersecurity and Infrastructure Security Agency 2020), ultimately the final decision was made by state-
level governance. Classification of some businesses was uniform across states, but others were less clear;
early in the pandemic, Pennsylvania was the only state to shutter liquor stores, Delaware permitted florists
to continue with deliveries, and Arizona allowed golf courses to remain open (Andrew 2020). Such
differences highlight the substantial influence of policymakers in determining the livelihood of employees
in industries that lack clear classification.
Despite existing variation in these classifications, essential workers broadly work in positions of
higher risk and lower pay. Many of these industries – including grocery stores, warehousing, public
transit, and health care – require employees to work long hours in high-density settings with prolonged
close contact with other individuals; such conditions may put these workers at increased risk for exposure
to Covid-19 (CDC 2020). Prior reports have also indicated that, compared to their non-essential
counterparts, essential workers are more likely to be Black, have a household income of less than
$40,000, and are less likely to hold a college degree (Kearney and Munana 2020). In addition, essential
workers are more likely to report having more difficulty affording necessities like utilities and food and
having more difficulty paying credit card bills (Kearney and Munana 2020). Furthermore, many essential
workers in frontline industries are over age 50, live in a household with one or more adults over age 65,
and have family care obligations (Rho, Brown, and Fremstad 2020). Some essential industries – such as
building cleaning services – also have a high incidence of uninsured workers (Rho, Brown, and Fremstad
2020). Thus, essential workers may simultaneously be at greater risk for Covid-19 infection and be less-
equipped to support themselves and their families in the event of illness. On the other hand, non-essential
7
workers may face a high risk of having reduced work hours or of becoming unemployed (Lund et al.
2020, Sanchez et al. 2020).
3. Data
We use medical claims data, which shed light on the clinical status of an individual member vis-à-vis
Covid-19 through a diagnosis code. Beyond information directly related to Covid-19, the medical claims
also allow us to capture relevant clinical characteristics, such as whether the member has been diagnosed
with a chronic condition in the past 12 months or whether the member has had an acute inpatient
hospitalization in the past 12 months. We focus on Covid-19 positivity rather than the downstream risk of
hospitalization or death for two reasons. First, the classification of businesses and their workers as
essential versus non-essential was not based on individuals’ differential risk of a positive diagnosis for
Covid-19 translating into hospitalization and/or death; in other words, the non-essential business closure
policy was orthogonal to one’s risk of hospitalization or death. Second, the downstream risk of
hospitalization or death is confounded by various factors including health care utilization (e.g., hospital
capacity) and treatment protocols (e.g., Covid-19 standard of care, which has rapidly evolved since the
beginning of the pandemic). These factors are less problematic in our study, which focuses on the
differential risk of Covid-19 transmission as a result of the non-essential business closure policy.
We also draw on member files, which provide information regarding a member’s demographic
characteristics. At the member level, we can observe the member’s age, gender, and ZIP code of
residence. By merging member ZIP codes with data from the Federal Office of Rural Health Policy and
the 2018 American Community Survey (ACS) 5-year aggregate ZIP Code tabulation area-level files, we
can also observe ZIP Code-level characteristics including the rurality of each member’s residence, its
racial and ethnic composition, and the percent of members ages 18 to 64 living below the Federal Poverty
Line (FPL). The member files also allow us to identify members as either primary policyholders or as
dependents of a primary policyholder (including spouses) and create a flag for members (either primary
policyholders or dependents) that share the same address (i.e., are cohabitants).
8
Our data also shed light on a member’s employment status and affiliation by maintaining NAICS
codes for employer-based customers. NAICS codes were developed by the Office of Management and
Budget (OMB) and adopted in 1997 (United States Census Bureau 2020a). Under NAICS, business
establishments are classified according to Sector (2-digit code), Subsector (3-digit code), Industry Group
(4-digit code), NAICS Industry (5-digit code), and National Industry (6-digit code) (United States Census
Bureau 2020b). Using each employer’s 4-digit Industry Group code, we classify individual members who
are primary policyholders as essential workers or non-essential workers.
For our analyses, we restrict our sample to primary policyholders and their cohabiting dependents
who reside in Pennsylvania and have been continuously enrolled and employed for at least four months
from January through April of 2020. We also restrict to members who are commercially insured; in other
words, we exclude members who are insured through Medicare or the Affordable Care Act. All analyses
are conducted for weeks 7 to 23 of 2020 (February 12, 2020 to June 9, 2020, inclusive), with the end date
coinciding with the expiration of the stay-at-home order declared by Governor Wolf (Governor Tom Wolf
2020b). Our final sample comprises 415,958 primary policyholders and 387,412 cohabiting dependents.
4. Descriptive Statistics
4.1. Trends
Figure 1a shows the cumulative proportion of members in our sample with a positive diagnosis for Covid-
19 over weeks 7 to 23 of 2020. From week 7 to week 9, nearly no members were positive for Covid-19.
Beginning in week 10, the proportion of Covid-positive members started to increase at a steady rate.
When we stratify members into essential workers versus non-essential workers, we see that this rate of
increase, and the cumulative proportion of Covid-positive members, is greater for essential workers
compared to their non-essential counterparts.
----------------------- Insert Figure 1 About Here -----------------------
It is possible that the disparities in Covid-positive rates across the two groups arise from
differential testing rates between essential workers and non-essential workers. Specifically, populations
9
with greater exposure to the virus may have been tested more frequently; in Pennsylvania, early
guidelines limited testing to people demonstrating Covid-19 symptoms (Moselle and Benshoff 2020). By
July 10 (which falls after the end of our study period), testing guidelines were revised to include
asymptomatic individuals who suspected contact with an infected person (Pennsylvania Department of
Health 2020). As such, we examine the weekly proportion of positive diagnoses among essential workers
who were tested and non-essential workers who were tested (Figure 1b). We find that the positivity rates
among essential and non-essential workers who were tested were comparable up until week 14. In fact,
prior to week 13, non-essential workers briefly exhibited higher rates of positivity than their essential
counterparts. Starting in week 13 and through week 18, a greater proportion of essential workers being
tested were receiving positive results compared to non-essential workers being tested. Week 19 and later,
the weekly positivity rates across the two groups become similar again. Overall, the weekly positivity rate
among all tested members began to exhibit a gradual decrease beginning in week 18.
4.2. Summary statistics
In Table 1, we present summary statistics of all primary policyholders and their cohabitants who meet our
inclusion criteria. In Panel A, we see that essential workers comprise 37.2% of primary policyholders, and
these members are more likely to be Covid-positive (2.1% positivity) than their non-essential counterparts
(0.9% positive). Overall, primary policyholders who are essential workers tend to be younger, are more
likely to be female, have slightly more rural representation, are less likely to have a chronic condition, and
are less likely to be cohabiting with another Independence member. Comparing ZIP Code-level
characteristics shows that essential workers are more likely to live in areas with a greater proportion of
Black/African American or Hispanic residents and are more likely to live in an area with a greater
proportion of residents living below the FPL.
----------------------- Insert Table 1 About Here -----------------------
When we turn to cohabiting dependents of primary policyholders (Panel B), we find that 33.7%
live with essential workers, whereas 66.3% live with non-essential workers. Mirroring what we saw with
10
the primary policyholders, dependents who live with essential workers are more likely to be Covid-
positive (0.59%) compared to those who live with non-essential workers (0.48%).
Panel C shows summary statistics for members who are cohabiting with another primary
policyholder but are neither their dependents nor essential workers themselves; we refer to this group of
members as non-essential non-dependents. We see that the overwhelming majority (94.5%) of these
members are living with other non-essential workers and only 5.5% are living with essential workers.
Those cohabiting with essential workers, compared to those cohabiting with non-essential workers, have a
higher likelihood of being Covid-positive (1.47% versus 0.89%).
5. Effect of being an essential worker
To quantify the average effect of the non-essential business closure order in Pennsylvania on the
likelihood of being Covid-positive among essential workers versus non-essential workers, we estimate a
difference-in-differences model on our sample of primary policyholders. We restrict our sample to
primary policyholders because we can only identify the employer industry of primary policyholders, and
thus classify them as essential versus non-essential workers. To account for the approximately two-week
incubation period of Covid-19 (Lauer et al. 2020), we define week 14 of 2020 (starting April 1, 2020) as
the beginning of the post-implementation period, which is two weeks after the business closure order was
enacted.1 Our difference-in-differences model is a fixed-effects regression as follows:
𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1(𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 ∗ 𝑃𝑃𝑃𝑃𝐸𝐸𝐸𝐸𝑖𝑖) + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 + 𝛽𝛽3𝐗𝐗𝑖𝑖 + 𝛽𝛽4𝑊𝑊𝐸𝐸𝐸𝐸𝑊𝑊𝑖𝑖 +
𝛽𝛽5𝐶𝐶𝑃𝑃𝐶𝐶𝐸𝐸𝐸𝐸𝐶𝐶𝑖𝑖 + 𝛽𝛽6𝐼𝐼𝐸𝐸𝐼𝐼𝐶𝐶𝐸𝐸𝐸𝐸𝐼𝐼𝐶𝐶𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 (1)
𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖 is a binary indicator for member 𝐸𝐸 in industry subsector 𝑗𝑗 in week 𝐸𝐸 and indicates whether the member
is positive for Covid-19. 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖 is an indicator variable that equals 1 if subsector 𝑗𝑗 in which the
member is employed is deemed essential by the governor’s business closure order, and 0 otherwise. 𝑃𝑃𝑃𝑃𝐸𝐸𝐸𝐸𝑖𝑖
1 As a robustness check in section 8, we also estimate a model in which we define week 12 of 2020 as the start of the post-implementation period.
11
equals 1 for weeks 14 through 23, and 0 otherwise. 𝐗𝐗𝑖𝑖 is a vector of member characteristics, including
age, gender, cohabitation status, clinical characteristics, rurality, and socioeconomic characteristics based
on the member’s ZIP Code. 𝑊𝑊𝐸𝐸𝐸𝐸𝑊𝑊𝑖𝑖, 𝐶𝐶𝑃𝑃𝐶𝐶𝐸𝐸𝐸𝐸𝐶𝐶𝑖𝑖, and 𝐼𝐼𝐸𝐸𝐼𝐼𝐶𝐶𝐸𝐸𝐸𝐸𝐼𝐼𝐶𝐶𝑖𝑖 are week, county, and industry fixed
effects, respectively. We control for industry fixed effects specifically at the subsector level (3-digit
NAICS code) to allow for within-industry variation of essential versus non-essential workers (since the
latter is defined at the 4-digit industry group level). The main effect for 𝑃𝑃𝑃𝑃𝐸𝐸𝐸𝐸𝑖𝑖 is omitted because it is
perfectly collinear with the week fixed effects. 𝛽𝛽1 is the difference-in-differences estimator that captures
the effect of the business closure order on the likelihood of being Covid-positive for essential workers.
Table 2 presents results from the difference-in-differences estimation. We find that being an
essential worker is associated with a 0.75 percentage point increase in one’s likelihood of being Covid-
positive (column (1)). Given an average positivity rate of 1.36%, this corresponds to a 55% increase in
likelihood of being Covid-positive for essential workers compared to non-essential workers.
----------------------- Insert Table 2 About Here -----------------------
To account for the possibility that some primary policyholders may no longer be active members
of the workforce (i.e., retired) but continue to receive health insurance benefits through their previous
employers, we repeat this estimation and restrict the sample to primary policyholders who are less than 65
years of age. This is particularly important to examine since older age is a documented risk factor for
Covid-19 (Jordan, Adab, and Cheng 2020), and thus could bias our findings away from the null. In Table
2 column (2), we see that our results are robust to this additional restriction. In this group, we find that
being an essential worker is associated with a 53% increase in likelihood of being Covid-positive (0.73
percentage point increase over an average positivity rate of 1.37%).
Next, we examine whether and the extent to which this effect is being driven by those who are
employed in the health care industry, as these members may disproportionately be exposed to others who
are positive for Covid-19. For this, we conduct two additional analyses. First, we repeat our estimation of
Equation (1) for a sample that excludes members who are employed in the health care and social
12
assistance subsector. Second, we use a sample that is comprised only of these members who are employed
in the health care and social assistance subsector. We include the social assistance subsector along with
the health care subsector because this is the highest level of granularity we can attain using the 3-digit
NAICS code. Examples of employers in this subsector include hospital systems, nursing homes, social
assistance, and daycare centers. In this subsector, 98% of primary policyholders are designated as
essential workers and 2% of primary policyholders are designated as non-essential workers, according to
the Pennsylvania governor’s business closure order.
The results of these analyses are shown in columns (3) and (4) of Table 2. When we exclude
members who are employed in the health care and social assistance subsector, we find that being an
essential worker is associated with a 21% increase in one’s likelihood of being Covid-positive (0.21
percentage point increase over an average positivity rate of 0.10%), relative to being a non-essential
worker. While smaller in magnitude than the estimated effect for the full sample, this result is still
statistically significant at the 0.1% level. In comparison, being an essential worker who is employed in the
health care and social assistance subsector is associated with a 41% increase in one’s likelihood of being
Covid-positive (1.24 percentage point increase over an average positivity rate of 3.02%), relative to being
a non-essential worker employed in the same subsector. The magnitude of the percentage point increase
(1.24 percentage points) is meaningfully larger than that of the estimated effect for the full sample (0.75
percentage points). These findings suggest that while those employed in the health care and social
assistance subsector contribute significantly to the overall effect, the effect is not completely driven by
these members.
6. Effect of cohabiting with an essential worker
The current clinical literature suggests that Covid-19 primarily spreads through close person-to-person
contact (Chu et al. 2020). Thus, we examine whether one’s risk of being Covid-positive after the business
closure order varies depending on whether the individual cohabits with an essential worker as opposed to
13
a non-essential worker. We separately examine the effects of cohabitation for (a) dependents of primary
policyholders and for (b) non-essential non-dependents cohabiting with another primary policyholder.
6.1. Effect on dependents cohabiting with an essential worker
First, we examine whether and the extent to which dependents of essential workers are at greater risk of
being Covid-positive relative to dependents of non-essential workers. In other words, are family members
of essential workers at greater risk of being positive for Covid-19? We repeat our estimation of Equation
(1) with an analysis sample that comprises all cohabiting dependents of primary policyholders. Column
(1) of Table 3 shows that dependents who are cohabiting with an essential worker are 0.09 percentage
points more likely to be Covid-positive than dependents who are cohabiting with a non-essential worker.
Given an average positivity rate of 0.51% among all dependents, this corresponds to a 17% increase in
likelihood of being Covid-positive for dependents of essential workers compared to dependents of non-
essential workers.
----------------------- Insert Table 3 About Here -----------------------
In columns (2) and (3), we further investigate whether this risk is different for dependents who
are 18 years of age or older (i.e., spouses or adult children) versus dependents who are under 18 (i.e.,
children who are minors). We find that the magnitude of the percentage-point change is larger for adult
dependents (0.13 percentage point increase) compared to minor dependents (0.03 percentage point
increase), but that the corresponding percent increases in their likelihood of being Covid-positive are
quite similar (17% for adult dependents versus 18% for minor dependents). This points to a substantial
difference in underlying positivity rates among the two groups (0.8% among adult dependents versus
0.1% among minor dependents).
6.2. Effect on non-essential non-dependents cohabiting with an essential worker
Next, we examine whether and the extent to which non-dependent cohabitants who are non-essential
workers are more likely to be Covid-positive when they live with an essential worker as opposed to a non-
essential worker. For the most part, we can think of these individuals as roommates who are not family
members (although it is possible that two family members may both be primary policyholders on separate
14
insurance policies with Independence). For the sample of all non-essential primary policyholders who are
cohabiting with another Independence primary policyholder, we again estimate Equation (1) and show
these results in column (4) of Table 3.
We find that non-essential non-dependents cohabiting with an essential worker experience a 0.35
percentage point increase in their likelihood of being Covid-positive relative to those cohabiting with a
non-essential worker. Given an average positivity rate of 0.93%, this corresponds to a 38% increase in
likelihood of being Covid-positive for individuals with a roommate who is an essential worker.
Interestingly, the magnitude of this increase in risk is greater for roommates (i.e., non-dependents) than
for family members (i.e., dependents).
7. Effect of cohabiting with a Covid-19 positive essential worker
To examine whether our analyses above actually represent the transmission of Covid-19 from one
individual to another who are living in the same household, we conduct additional analyses in which we
focus on a sample of households with at least one Covid-positive member. After identifying this set of
households, we remove from the analysis sample the Covid-positive index member, which we define as
the household member with the earliest diagnosis date. This leaves us with an analysis sample of
individuals cohabiting with a Covid-positive member who may either be an essential or a non-essential
worker. In 167 households (0.04% of the full sample), two members of the same household are diagnosed
with Covid-19 on the same date. Of these, 161 households have Covid-positive members with a
concordant essential status (i.e., both members are either essential workers or non-essential workers) and
6 households have Covid-positive members with discordant essential status (i.e., one member is an
essential worker and the other is a non-essential worker). For these 167 households, we randomly assign
one of the two Covid-positive members as the index member and remove that individual from the analysis
sample. We also drop the 6 discordant households from our analyses and find our results to be robust.
Column (5) of Table 3 reports the results of estimating Equation (1) for this sample of individuals
cohabiting with a Covid-19 positive index member. We find that those cohabiting with a Covid-positive
15
essential worker have a likelihood of Covid-positivity themselves that is 0.45 percentage points higher
than those cohabiting with a Covid-positive non-essential worker (column (1)). Given a relatively high
positivity rate in this population (7.4%), this corresponds to a 6.1% increase in likelihood of being Covid-
positive for those living with a Covid-positive essential worker in comparison to those living with a
Covid-positive non-essential worker. This suggests that intrahousehold transmission of Covid-19 is high
regardless of whether the index member is an essential worker or not, and that the risk of transmission
seems slightly higher when the index member is an essential worker.
8. Robustness
We conducted several additional analyses to assess the robustness of our main findings. First, we find that
our difference-in-differences estimates are highly stable across the inclusion of fewer versus more control
variables and fixed effects. For our main model with the sample of primary policyholders, as we move
from a sparser model with only week fixed effects (Table 4 column (1)) to models in which we add
member- and ZIP Code-level controls (column (2)), and county fixed effects (column (3)), the effect
remains remarkably robust and consistent. This is also the case for all other models estimated above (see
tables in the Appendix).
----------------------- Insert Table 4 About Here -----------------------
The main effect is also robust to defining week 12 of 2020 as the start of the post-implementation
period as opposed to week 14. Week 12 marks the very beginning of the business closure order and does
not account for the time it takes for the virus to spread, incubate, and manifest via symptoms. Since the
criteria for receiving a diagnostic test for Covid-19 during the study period was relatively strict and
largely limited test availability to symptomatic individuals or those with close contact with a known
Covid-positive individual (Pennsylvania Department of Health 2020), using week 12 to define the
beginning of the post period may provide an underestimate of the true effect as it does not sufficiently
account for a “lead-in” period. We find that our main results remain highly robust, with a 0.65 percentage
point increase in likelihood of being Covid-positive associated with being an essential worker as opposed
16
to a non-essential worker (column (4)); this effect is only marginally smaller in magnitude than what we
estimated previously in section 5.
9. Discussion and Conclusions
Our findings suggest that essential workers and their cohabitants (whether dependents or other primary
policyholders sharing the same address) are at substantially higher risk of being positive for Covid-19
than are non-essential workers and their cohabitants. Conversely, non-essential workers and their
cohabitants experience a protective effect against the risk of Covid-19 infection as a result of the non-
essential business closure policy. In our sample, essential workers are 55% more likely to be Covid-
positive than non-essential workers. Family members (i.e., dependents) who live with essential workers
are 17% more likely to be Covid-positive compared to family members living with non-essential workers.
Also at increased risk for Covid-19 are roommates (non-dependents) who are not essential workers
themselves but are living with an essential worker; these individuals are 38% more likely to be Covid-
positive than are roommates of non-essential workers. However, conditional on the presence of a Covid-
positive household member, the increased risk of transmission to other members of the household when
the first Covid-positive member in the household is an essential worker (as opposed to a non-essential
worker) is marginal; there is a 6% increase in one’s likelihood of becoming Covid- positive when living
with a Covid-positive essential worker as opposed to a Covid-positive non-essential worker.
We acknowledge there are several limitations to our analyses. First, our analyses are based on
data from a single insurer and its commercially insured members. Given that Independence is the largest
health insurer in the Philadelphia, Pennsylvania area, our findings are likely to be representative of
commercially insured individuals in this region, but may not be completely generalizable to other
populations. Notably absent from our sample are uninsured individuals or those who were previously
commercially insured but recently lost their health insurance, since health insurance in this setting is tied
to employment. In this population, the distribution of essential workers and non-essential workers may be
different from what is observed in our sample of commercially insured individuals. If we were to assume
17
that those who are not commercially insured are in fact more likely to serve as essential workers (e.g.,
part-time workers at grocery stores, PRN (pro re nata) home health aides, delivery drivers), then our
estimates may be biased towards the null and offer a conservative estimate regarding the risk borne by
essential workers of becoming positive for Covid-19. Another limitation is that our data do not allow us
to fully account for differences among members in their socioeconomic status or other factors that may
impact both one’s likelihood of being an essential worker and living in an environment with higher risks
of Covid transmission (e.g., living in a tighter space with more cohabitants).
Finally, it is possible that the information regarding dependents’ addresses may not be completely
accurate, as members may not always update dependents’ addresses when there is a change. In some
cases, dependents who were previously living elsewhere may have recently started cohabiting with the
primary policyholder (e.g., college students who moved back in with their parents when universities
depopulated their campuses); our analyses would have failed to capture these dependents as cohabitants,
leaving them out of the analysis sample altogether in estimating the effect of cohabiting with an essential
worker. In other cases, dependents who were previously living with the primary policyholder may have
left (e.g., children who moved out of their parents’ home but did not update their addresses with
Independence); our analyses would have counted these dependents as cohabitants. However, doing so
would have biased our results towards the null since it is less likely that the primary policyholder would
have served as a transmission vector for this dependent who is not located under the same roof.
Furthermore, even if these dependents were not cohabiting with the primary policyholder, their likelihood
of in-person interaction may be higher than not, which would serve as another potential vector of
transmission.
The societal tradeoffs between health and economic viability seen during the Covid-19 pandemic
evoke economic estimates of the “value of a statistical life” (Viscusi 1993). Previously, most of this
literature has concentrated on valuing mortality risk by estimating compensating differentials for on-the-
job risk exposure in labor markets. Because increases in health risks can be detrimental, economists
believe that there must be some other aspect of the job that compensates for the added risk (Viscusi and
18
Aldy 2003) – for example, receiving higher pay or greater job satisfaction. In contrast, the designation of
some workers as essential and others as non-essential during the pandemic has increased the health risk
profile of some jobs while reducing it for others, all while other underlying aspects of these jobs (e.g.,
monetary compensation) remain minimally affected. Thus, in the case of the Covid-19 pandemic, rather
than a risk-income tradeoff, the value of a statistical life approach takes more of a risk-risk tradeoff and
focuses our attention on the net effect of a policy or regulation on a population’s risk exposure (Viscusi
1994). For example, by designating grocery stores as essential businesses, policymakers ensured access to
food (and thus, reduced the risk of food insecurity) while raising the risk of exposure to and spread of the
virus for those who work in that industry. Given these consequential tradeoffs, policymakers must assess
the benefits of increased health risks when deciding on the extent of economic activity. These policy
choices ultimately involve a balancing of increased health risk and other risks.
As policymakers weigh the risks and benefits of reopening economies and allowing for the
resumption of regular in-person interactions, person-to-person transmission of Covid-19 remains a
primary concern. Our estimates illustrate the differential impact of Covid-19 on essential versus non-
essential workers, though we note that the magnitude of the differences in risk experienced by essential
versus non-essential workers will decrease as the overall positivity rate decreases over time. Even when
accounting for the influence of health care workers on Covid-positivity, we find that a worker’s essential
status puts the individual – and their family members – at higher risk for Covid-19 infection. Thus, our
results show that the designation of a workplace as essential or non-essential by state-level governance is
one that may have serious health and safety implications for the workers affected. These findings should
be taken into consideration as bodies of government determine how best to lead society forward as the
Covid-19 pandemic persists.
19
References Andrew, Scottie. 2020. "What constitutes 'essential businesses'? States seem to have varying standards."
Accessed August 27, 2020. https://www.cnn.com/2020/03/25/business/essential-businesses-states-coronavirus-trnd/index.html.
Cai, Jiehao, Jin Xu, Daojiong Lin, Zhi Yang, et al. 2020. "A Case Series of Children With 2019 Novel Coronavirus Infection: Clinical and Epidemiological Features." Clinical Infectious Diseases. doi: 10.1093/cid/ciaa198.
CDC. 2020. "Testing in High-Density Critical Infrastructure Workplaces." Accessed August 27, 2020. https://www.cdc.gov/coronavirus/2019-ncov/community/worker-safety-support/hd-testing.html.
Chen, Simiao, Zongjiu Zhang, Juntao Yang, Jian Wang, Xiaohui Zhai, Till Bärnighausen, and Chen Wang. 2020. "Fangcang shelter hospitals: a novel concept for responding to public health emergencies." The Lancet 395 (10232):1305-1314. doi: https://doi.org/10.1016/S0140-6736(20)30744-3.
Chu, Derek K., Elie A. Akl, Stephanie Duda, Karla Solo, et al. 2020. "Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: a systematic review and meta-analysis." The Lancet 395 (10242):1973-1987. doi: https://doi.org/10.1016/S0140-6736(20)31142-9.
Ciminelli, Gabriele, and Silvia Garcia-Mandico. 2020. "Mitigation Policies and Emergency Care Management in Europe's Ground Zero for COVID-19." SSRN. doi: http://dx.doi.org/10.2139/ssrn.3604688.
Curmei, Mihaela, Andrew Ilyas, Owain Evans, and Jacob Steinhardt. 2020. "Estimating Household Transmission of SARS-CoV-2." medRxiv:2020.05.23.20111559. doi: 10.1101/2020.05.23.20111559.
Cybersecurity and Infrastructure Security Agency. 2020. "Guidance on the Essential Critical Infrastructure Workforce: Ensuring Community and National Resilience in COVID-19 Response." Accessed August 27, 2020. https://www.cisa.gov/sites/default/files/publications/Version_4.0_CISA_Guidance_on_Essential_Critical_Infrastructure_Workers_FINAL%20AUG%2018v3.pdf.
del Rio-Chanona, R. Maria , Penny Mealy, Anton Pichler, Francois Lafond, and Doyne Farmer. 2020. "Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective." Covid Economics (6):65-103.
Fairlie, Robert W, Kenneth Couch, and Huanan Xu. 2020. "The Impacts of COVID-19 on Minority Unemployment: First Evidence from April 2020 CPS Microdata." National Bureau of Economic Research Working Paper Series No. 27246. doi: 10.3386/w27246.
Fischer, Carolyn. 2020. "Different measures for different people." Covid Economics (25):1-22. Forsythe, Eliza, Lisa B Kahn, Fabian Lange, and David G Wiczer. 2020. "Labor Demand in the time of
COVID-19: Evidence from vacancy postings and UI claims." National Bureau of Economic Research Working Paper Series No. 27061. doi: 10.3386/w27061.
Goolsbee, Austan, and Chad Syverson. 2020. "Fear, Lockdown, and Diversion: Comparing Drivers of Pandemic Economic Decline 2020." National Bureau of Economic Research Working Paper Series No. 27432. doi: 10.3386/w27432.
Governor Tom Wolf. 2020a. "All Non-Life-Sustaining Businesses in Pennsylvania to Close Physical Locations as of 8 PM Today to Slow Spread of COVID-19." Accessed June 23, 2020a. https://www.governor.pa.gov/newsroom/all-non-life-sustaining-businesses-in-pennsylvania-to-close-physical-locations-as-of-8-pm-today-to-slow-spread-of-covid-19/.
Governor Tom Wolf. 2020b. "Gov. Wolf Renews COVID-19 Disaster Declaration for State Response and Recovery, Stay-at-Home Order Ends June 4." Accessed December 17, 2020b. https://www.governor.pa.gov/newsroom/gov-wolf-renews-covid-19-disaster-declaration-for-state-response-and-recovery-stay-at-home-order-ends-june-4/#:~:text=Wolf%20Renews%20COVID-
20
19%20Disaster,Home%20Order%20Ends%20June%204&text=COVID-19%20cases%20are%20at,on%20COVID-19%20in%20Pennsylvania.
International Labour Organization. 2020. "ILO Monitor: COVID-19 and the world of work. 5th edition." Accessed September 7, 2020. https://www.ilo.org/global/topics/coronavirus/impacts-and-responses/WCMS_749399/lang--en/index.htm.
Jordan, Rachel E, Peymane Adab, and K K Cheng. 2020. "Covid-19: risk factors for severe disease and death." BMJ 368:m1198. doi: 10.1136/bmj.m1198.
Kearney, Audrey, and Cailey Munana. 2020. "Taking Stock of Essential Workers." Accessed August 27, 2020. https://www.kff.org/policy-watch/taking-stock-of-essential-workers/.
Lauer, S. A., K. H. Grantz, Q. Bi, F. K. Jones, Q. Zheng, H. R. Meredith, A. S. Azman, N. G. Reich, and J. Lessler. 2020. "The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application." Ann Intern Med 172 (9):577-582. doi: 10.7326/M20-0504.
Liu, Jiaye, Xuejiao Liao, Shen Qian, Jing Yuan, Fuxiang Wang, Yingxia Liu, Zhaoqin Wang, Fu-Sheng Wang, Lei Liu, and Zheng Zhang. 2020. "Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020." Emerging Infectious Diseases 26 (6):1320-1323. doi: 10.3201/eid2606.200239.
Lund, Susan, Kweilin Ellingrud, Bryan Hancock, James Manyika, and Andre Dua. 2020. "Lives and livelihoods: Assessing the near-term impact of COVID-19 on US workers." Accessed September 20, 2020. https://www.mckinsey.com/industries/public-and-social-sector/our-insights/lives-and-livelihoods-assessing-the-near-term-impact-of-covid-19-on-us-workers.
Moselle, Aaron, and Laura Benshoff. 2020. "Coronavirus update: Pa. expands testing recommendations." Accessed August 27, 2020. https://whyy.org/articles/coronavirus-update-nearly-1-in-4-pennsylvanians-has-applied-for-unemployment/.
Mutambudzi, Miriam, Claire L Niedzwiedz, Ewan B Macdonald, Alastair H Leyland, et al. 2020. "Occupation and risk of severe COVID-19: prospective cohort study of 120,075 UK Biobank participants." medRxiv:2020.05.22.20109892. doi: 10.1101/2020.05.22.20109892.
NHIS: National Center for Health Statistics (NCHS). National Vital Statistics Survey 2014-2016. Hyattsville, MD: Centers for Disease Control and Prevention, U.S. Department of Health and Human Services. .
Pennsylvania Department of Health. 2020. "Coronavirus Symptoms & Testing." Accessed August 31, 2020. https://www.health.pa.gov/topics/disease/coronavirus/Pages/Symptoms-Testing.aspx.
Rho, Hye Jin, Hayley Brown, and Shawn Fremstad. 2020. "A Basic Demographic Profile of Workers in Frontline Industries." Accessed August 27, 2020. https://cepr.net/a-basic-demographic-profile-of-workers-in-frontline-industries/.
Sanchez, Daniel Garrote, Nicolas Gomez Parra, Caglar Ozden, and Bob Rijkers. 2020. "Which jobs are the most at risk because of COVID-19?", Accessed September 20, 2020. https://www.brookings.edu/blog/future-development/2020/05/18/which-jobs-are-most-at-risk-because-of-covid-19/.
Schiavone, Ansel. 2020. "Essentially Unemployed: Potential Implications of the COVID-19 Crisis on Wage Inequality." Working Paper Series, Department of Economics, University of Utah, University of Utah, Department of Economics.
Tan, Y. P., B. Y. Tan, J. Pan, J. Wu, S. Z. Zeng, and H. Y. Wei. 2020. "Epidemiologic and clinical characteristics of 10 children with coronavirus disease 2019 in Changsha, China." J Clin Virol 127:104353. doi: 10.1016/j.jcv.2020.104353.
United States Census Bureau. 2020a. "Introduction to NAICS." Accessed June 23, 2020a. https://www.census.gov/eos/www/naics/.
United States Census Bureau. 2020b. "NAICS Codes." Accessed June 23, 2020b. https://www.census.gov/programs-surveys/economic-census/guidance/understanding-naics.html.
United States Department of Labor. 2020. "Unemployment Insurance Data." Accessed September 7, 2020. https://oui.doleta.gov/unemploy/DataDashboard.asp.
21
Viscusi, W. Kip. 1993. "The Value of Risks to Life and Health." Journal of Economic Literature 31:1912-1946.
Viscusi, W. Kip. 1994. "Risk-Risk Analysis." Journal of Risk and Uncertainty 8 (1):15-17. Viscusi, W. Kip, and Joseph E. Aldy. 2003. "The Value of a Statistical Life: A Critical Review of Market
Estimates Throughout the World." Journal of Risk and Uncertainty 27 (1):5-76. doi: 10.1023/A:1025598106257.
22
Figure 1. Trends of Covid-19 positivity, week 7 to week 23 of 2020
Notes. Panel (a) shows the cumulative percent of members who are positive for Covid-19, out of all Independence members. Panel (b) shows the weekly percent of members who are positive for Covid-19, out of tested Independence members. In both panels, the dotted line at week 12 indicates when the governor’s non-essential business closure order was enacted in Pennsylvania, and the dotted line at week 14 marks the beginning of the post-implementation period in our analyses; the latter accounts for the approximately two-week incubation period of Covid-19.
23
Table 1. Summary statistics for primary policyholders and their cohabitants
[1] Data come from the Federal Office of Rural Health Policy data files and the 2018 American Community Survey (ACS) 5-year aggregate ZIP Code tabulation area-level files. [2] Clinical characteristics are measured using a 12-month lookback window. [3] Chronic conditions include acquired hypothyroidism; acute myocardial infarction; Alzheimer’s disease; Alzheimer’s disease and related disorders or senile dementia; anemia; asthma; atrial fibrillation; benign prostatic hyperplasia; breast cancer; cataract; cerebral palsy; chronic kidney disease; chronic obstructive pulmonary disease and bronchiectasis; colorectal cancer; cystic fibrosis and other metabolic developmental disorders; diabetes type 1; diabetes type 2; endometrial cancer; epilepsy; fibromyalgia, chronic pain, and fatigue; glaucoma; heart failure; hip/pelvic fracture; hepatitis A; hepatitis B; hepatitis C; hepatitis D; hepatitis E; hyperlipidemia; hypertension; ischemic heart disease; leukemias and lymphomas; lung cancer; migraine and chronic headache; mobility impairments; multiple sclerosis and transverse myelitis; muscular dystrophy; osteoporosis; peripheral vascular disease; prostate cancer; rheumatoid arthritis/osteoarthritis; sensory blindness and visual impairment; sensory deafness and hearing impairment; spina bifida and other congenital anomalies of the nervous system; spinal cord injury; stroke/transient ischemic attack; and traumatic brain injury and nonpsychotic mental disorders due to brain damage.
All primary policyholders Cohabiting dependents Cohabiting non-essential non-dependents
Essential Non-essential Difference
Living with
essential primary policy-holder
Living with non-essential primary policy-holder
Difference
Living with
essential primary policy-holder
Living with non-essential primary policy-holder
Difference
Member-level characteristics N (%) N (%) p-value N (%) N (%) p-value N (%) N (%) p-value
Covid-19 positive 3218 (2.1) 2438 (0.9) <0.001 766 (0.6) 1226 (0.5) <0.001 123 (1.5) 1279 (0.9) <0.001 Age (years) <0.001 <0.001 <0.001
0-17 8 (0.005) 34 (0.01) 0.23 56226 (43.0)
109800 (42.8) 0.09 2 (0.0) 20 (0.0) 0.79
18-50 95393 (61.6)
149268 (57.2) <0.001 53970
(41.3) 104853 (40.8) 0.004 5337
(63.6) 78364 (54.8) <0.001
51-64 48720 (31.5)
82797 (31.7) 0.12 17478
(13.4) 34834 (13.6) 0.111 2433
(29.0) 50189 (35.1) <0.001
65+ 10688 (6.9)
29050 (11.1) <0.001 2955 (2.3) 7296 (2.8) <0.001 616 (7.3) 14416
(10.1) <0.001
Female 86390 (55.8)
123653 (47.3) <0.001 68470
(52.4) 139179 (54.2) <0.001 3748
(44.7) 61840 (43.2) 0.01
Rural[1] 1686 (1.1) 2366 (0.9) <0.001 1398 (1.1) 2408 (0.9) <0.001 8 (0.1) 1314 (0.9) <0.001 Clinical characteristics[2]
At least one chronic condition[3]
72289 (46.7)
129296 (49.5) <0.001 44919
(34.4) 96031 (37.4) <0.001 3781
(45.1) 73244 (51.2) <0.001
At least one acute inpatient hospitalization
6929 (4.5) 11850 (4.5) 0.36 3860 (3.0) 7734 (3.0) 0.33 330 (3.9) 6899 (4.8) <0.001
Member with cohabiting dependent
65517 (42.3)
128746 (49.3) <0.001 N/A N/A N/A N/A N/A N/A
Member with cohabiting non-dependent
12679 (8.2)
22631 (8.7) <0.001 N/A N/A N/A N/A N/A N/A
ZIP Code-level characteristics % (SD) % (SD) p-value % (SD) % (SD) p-value % (SD) % (SD) p-value
% White 68.4 (27.3) 71.0 (25.6) <0.001 75.7 (22.5) 73.3 (24.7) <0.001 67.4 (27.0) 73.9 (23.8) <0.001 % Black or African American 20.2 (25.9) 17.9 (24.3) <0.001 13.9 (20.9) 16.1 (23.0) <0.001 20.7 (25.9) 15.4 (22.3) <0.001
% Asian 6.0 (5.2) 6.1 (5.1) <0.001 6.0 (4.8) 6.0 (4.9) 0.005 6.4 (5.3) 6.0 (4.9) <0.001 % Other race 2.8 (4.4) 2.5 (4.0) <0.001 2.1 (3.4) 2.3 (3.8) <0.001 2.9 (4.4) 2.2 (3.6) <0.001 % Hispanic or Latino 7.9 (9.4) 7.2 (8.6) <0.001 6.4 (7.4) 6.7 (8.0) <0.001 7.8 (9.1) 6.6 (7.8) <0.001
% below FPL, ages 18-64 12.0 (9.6) 11.1 (9.3) <0.001 9.1 (7.8) 9.9 (8.3) <0.001 11.9 (9.5) 9.8 (8.3) <0.001
Total members 154809 261149 130629 256783 8388 142989
24
Table 2. Effect of essential status on likelihood of being positive for Covid-19 (1)
All primary policyholders
(2) Restricted to
primary policyholders
<65 years
(3) Excluding primary
policyholders in health care and
social assistance subsector
(4) Restricted to
primary policyholders in health care and
social assistance subsector
Essential x Post 0.00751*** (0.00011)
0.0073*** (0.00012)
0.00209*** (0.00012)
0.01241*** (0.00137)
Essential -0.00369*** (0.00011)
-0.00357*** (0.00012)
-0.00088*** (0.00011)
-0.00186+ (0.00105)
Age <18 -0.00448+ (0.00265)
-0.00464+ (0.00267)
-0.00464* (0.00229)
-0.00795 (0.02579)
Age 51-64 0.00017** (0.00006)
0.00016** (0.00006)
0.00005 (0.00006)
0.00076*** (0.00022)
Age 65+ -0.00111*** (0.0001) -0.00145***
(0.00009) 0.00119** (0.00039)
Female -0.00003 (0.00006)
0.00003 (0.00006) 0 (0.00005) 0.00036
(0.00022)
Rural -0.00051+ (0.00029)
-0.00046 (0.0003)
-0.0002 (0.00027)
-0.00225* (0.00098)
At least one chronic condition 0.00246*** (0.00006)
0.00253*** (0.00006)
0.00188*** (0.00005)
0.00497*** (0.0002)
At least one acute inpatient hospitalization
0.00483*** (0.00013)
0.00453*** (0.00015)
0.00489*** (0.00012)
0.00483*** (0.00043)
Member with cohabiting dependent
0.00073*** (0.00006)
0.00065*** (0.00006)
0.0007*** (0.00005)
0.00066** (0.0002)
Member with cohabiting non-dependent
-0.00033** (0.0001)
-0.00037** (0.00012)
0.00002 (0.00009)
-0.00169*** (0.00038)
% Black or African American 0.01078*** (0.00018)
0.01097*** (0.00019)
0.00926*** (0.00018)
0.01491*** (0.00059)
% Asian 0.01131*** (0.00059)
0.0108*** (0.00062)
0.00891*** (0.00055)
0.02106*** (0.00203)
% Other race 0.0077*** (0.00205)
0.01134*** (0.00215)
0.00415* (0.00192)
0.00564 (0.00739)
% Hispanic or Latino of any race in ZIP Code
0.00694*** (0.00095)
0.00591*** (0.001)
0.00579*** (0.00088)
0.01579*** (0.00355)
% Below FPL in ZIP Code -0.00909*** (0.00056)
-0.01015*** (0.00058)
-0.00623*** (0.00053)
-0.0177*** (0.0018)
Week FE Yes Yes Yes Yes County FE Yes Yes Yes Yes Industry FE Yes Yes Yes No Mean of DV 0.0136 0.0137 0.0100 0.0302 Observations 415,958 376,220 341,118 74,840
Notes. The table presents results of estimating Equation (1) using a sample of all primary policyholders (column (1)), primary policyholders less than 65 years (column (2)), primary policyholders not in the health care and social assistance subsector (column (3)), and primary policyholders in the health care and social assistance subsector only (column (4)). Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
25
Table 3. Effect of cohabiting with essential worker on likelihood of being positive for Covid-19 (1)
All cohabiting dependents
(2) Cohabiting dependents, 18+ years
(3) Cohabiting dependents, <18 years
(4) Cohabiting
non-essential non-
dependents
(5) Cohabitants
of first Covid-positive
member in household
Essential x Post 0.00086*** (0.00007)
0.00133*** (0.00012)
0.00027*** (0.00005)
0.00349*** (0.00032)
0.00453** (0.00167)
Essential -0.00064*** (0.00007)
-0.00096*** (0.00012)
-0.00021*** (0.00005)
-0.00039 (0.00025)
-0.00888*** (0.00171)
Age <18 -0.00183*** (0.00004) -0.00522+
(0.00299) -0.01912***
(0.00097)
Age 51-64 0.00124*** (0.00005)
0.00118*** (0.00007) 0.00034***
(0.00008) 0.01933*** (0.00118)
Age 65+ 0.00087*** (0.00011)
0.00062*** (0.00014) -0.00148***
(0.00013) 0.01027*** (0.00181)
Female 0.00013*** (0.00003)
0.00019** (0.00006)
0.00005* (0.00002)
-0.00016* (0.00008)
0.00242** (0.00083)
Rural -0.00031+ (0.00018)
-0.00044 (0.00029)
-0.00013 (0.00013)
-0.00132** (0.00041)
0.01441 (0.01262)
At least one chronic condition
0.00099*** (0.00004)
0.00157*** (0.00006)
0.00008** (0.00003)
0.00185*** (0.00008)
0.01114*** (0.0009)
At least one acute inpatient hospitalization
0.00361*** (0.0001)
0.00343*** (0.00014)
0.0039*** (0.00011)
0.00499*** (0.00017)
0.02719*** (0.00207)
% Black or African American
0.00243*** (0.00013)
0.00366*** (0.00021)
0.00087*** (0.00009)
0.00855*** (0.00027)
-0.00756** (0.00248)
% Asian 0.00456*** (0.00038)
0.00751*** (0.00064)
0.0002 (0.00028)
0.00813*** (0.00082)
0.02981*** (0.00881)
% Other race 0.00128 (0.00134) 0.00164 (0.00223) 0.001
(0.00097) 0.01166*** (0.00301)
0.06123+ (0.03204)
% Hispanic or Latino of any race in ZIP Code
0.00134* (0.00061)
0.00173+ (0.00101)
0.00047 (0.00044)
0.00285* (0.00138)
-0.04437** (0.01577)
% Below FPL in ZIP Code
0.00054 (0.0004)
0.00202** (0.00068)
-0.00056* (0.00029)
-0.00761*** (0.00085)
0.02178** (0.0082)
Week FE Yes Yes Yes Yes Yes County FE Yes Yes Yes Yes Yes Industry FE Yes Yes Yes Yes Yes Mean of DV 0.00514 0.0079 0.00146 0.00926 0.0739 Observations 387,412 221,386 166,026 151,377 10,117
Notes. The table presents results of estimating Equation (1) using a sample of all cohabiting dependents (column (1)), cohabiting dependents 18 years or older (column (2)), cohabiting dependents less than 18 years (column (3)), cohabiting non-essential non-dependents (column (4)), and cohabitants of the first Covid-positive member in the household (column (5)). Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
26
Table 4. Robustness checks (1)
All primary policyholders
(2) All primary
policyholders
(3) All primary
policyholders
(4) All primary
policyholders; Post period
beginning week 12
Essential x Post 0.00751*** (0.00011)
0.00751*** (0.00011)
0.00751*** (0.00011)
0.00655*** (0.00012)
Essential 0.00021* (0.00009)
-0.00006 (0.00009)
-0.00001 (0.00009)
-0.0039*** (0.00013)
Age <18 -0.00474+ (0.00266)
-0.00454+ (0.00266)
-0.00448+ (0.00265)
Age 51-64 0.00016** (0.00006)
0.00028*** (0.00006)
0.00017** (0.00006)
Age 65+ -0.00116*** (0.0001)
-0.00114*** (0.0001)
-0.00111*** (0.0001)
Female 0.00026*** (0.00005)
0.00026*** (0.00005)
-0.00003 (0.00006)
Rural -0.00083** (0.00028)
-0.00015 (0.00029)
-0.00051+ (0.00029)
At least one chronic condition 0.00263*** (0.00006)
0.00262*** (0.00006)
0.00246*** (0.00006)
At least one acute inpatient hospitalization 0.00495***
(0.00013) 0.00493*** (0.00013)
0.00483*** (0.00013)
Member with cohabiting dependent 0.00078*** (0.00006)
0.00087*** (0.00006)
0.00073*** (0.00006)
Member with cohabiting non-dependent -0.00038***
(0.0001) -0.00041***
(0.0001) -0.00033**
(0.0001)
% Black or African American 0.01323*** (0.00017)
0.01168*** (0.00018)
0.01078*** (0.00018)
% Asian 0.01667*** (0.00054)
0.01171*** (0.00059)
0.01131*** (0.00059)
% Other race 0.02353*** (0.00198)
0.01067*** (0.00204)
0.0077*** (0.00205)
% Hispanic or Latino of any race in ZIP Code 0.002*
(0.00092) 0.00683*** (0.00095)
0.00694*** (0.00095)
% Below FPL in ZIP Code -0.00494*** (0.00052)
-0.00954*** (0.00056)
-0.00909*** (0.00056)
Week FE Yes Yes Yes Yes County FE No No Yes Yes Industry FE No No No Yes Mean of DV 0.0136 0.0136 0.0136 0.0136 Observations 415,958 415,958 415,958 415,958
Notes. The table presents results of estimating Equation (1) using a sample of all primary policyholders. All columns include week fixed effects. Column (2) adds member- and ZIP Code-level controls, and column (3) adds county fixed effects. Column (4) defines week 12 (as opposed to week 14) of 2020 as the start of the post-implementation period. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
27
APPENDIX Table A1. Summary statistics for Covid-positive primary policyholders and cohabitants of first Covid-19 positive member in household
Covid-positive primary policyholder Cohabitants of first Covid-positive member in household
Essential Non-essential Difference
Living with essential
index member
Living with non-essential
index member
Difference
Member-level characteristics N (%) N (%) p-value N (%) N (%) p-value
Covid-19 positive 3218 (100.0) 2438 (100.0) N/A 340 (7.5) 408 (7.3) 0.67
Age (years) <0.001 <0.001 0-17 0 (0.0) 0 (0.0) N/A 1435 (31.8) 1738 (31.0) 0.44 18-50 1934 (60.1) 1325 (54.3) <0.001 1984 (43.9) 2502 (44.7) 0.46 51-64 1036 (32.2) 874 (35.8) 0.004 768 (17.0) 1058 (18.9) 0.02 65+ 248 (7.7) 239 (9.8) 0.006 330 (7.3) 302 (5.4) <0.001 Female 2077 (64.5) 1171 (48.0) <0.001 2288 (50.7) 2855 (51.0) 0.76 Rural[1] 7 (0.2) 5 (0.2) 1.0 2 (0.04) 15 (0.3) 0.01 Clinical characteristics[2]
At least one chronic condition[3]
1892 (58.8) 1538 (63.1) 0.001 1874 (41.5) 2519 (45.0) <0.001
At least one acute inpatient hospitalization
223 (6.9) 246 (10.1) <0.001 188 (4.2) 242 (4.3) 0.73
Member with cohabiting dependent
1352 (42.0) 1216 (49.9) <0.001 N/A N/A N/A
Member with cohabiting non-dependent
260 (8.1) 186 (7.6) 0.567 N/A N/A N/A
ZIP Code-level characteristics % (SD) % (SD) p-value % (SD) % (SD) p-value
% White 53.6 (30.7) 55.4 (30.5) 0.03 58.6 (30.3) 63.0 (28.6) <0.001 % Black or African American
33.3 (31.2) 31.5 (31.1) 0.04 29.0 (30.3) 24.5 (28.4) <0.001
% Asian 6.5 (5.6) 6.5 (5.5) 0.81 6.5 (5.5) 6.6 (5.3) 0.45 % Other race 3.9 (5.4) 3.8 (5.5) 0.66 3.3 (4.8) 3.3 (4.7) 0.75 % Hispanic or Latino 9.9 (11.3) 9.6 (11.3) 0.44 8.6 (9.9) 8.6 (9.8) 0.78
% below FPL, ages 18-64 15.9 (10.5) 15.5 (10.6) 0.18 13.8 (9.9) 12.8 (9.6) <0.001
Total members 3218 2438 4517 5600
Notes. An index member is the first member in the household with a positive diagnosis for Covid-19. [1] Data come from the Federal Office of Rural Health Policy data files and the 2018 American Community Survey (ACS) 5-year aggregate ZIP Code tabulation area-level files. [2] Clinical characteristics are measured using a 4-month lookback window. [3] Chronic conditions include acquired hypothyroidism; acute myocardial infarction; Alzheimer’s disease; Alzheimer’s disease and related disorders or senile dementia; anemia; asthma; atrial fibrillation; benign prostatic hyperplasia; breast cancer; cataract; cerebral palsy; chronic kidney disease; chronic obstructive pulmonary disease and bronchiectasis; colorectal cancer; cystic fibrosis and other metabolic developmental disorders; diabetes type 1; diabetes type 2; endometrial cancer; epilepsy; fibromyalgia, chronic pain, and fatigue; glaucoma; heart failure; hip/pelvic fracture; hepatitis A; hepatitis B; hepatitis C; hepatitis D; hepatitis E; hyperlipidemia; hypertension; ischemic heart disease; leukemias and lymphomas; lung cancer; migraine and chronic headache; mobility impairments; multiple sclerosis and transverse myelitis; muscular dystrophy; osteoporosis; peripheral vascular disease; prostate cancer; rheumatoid arthritis/osteoarthritis; sensory blindness and visual impairment; sensory
28
deafness and hearing impairment; spina bifida and other congenital anomalies of the nervous system; spinal cord injury; stroke/transient ischemic attack; and traumatic brain injury and nonpsychotic mental disorders due to brain damage.
29
Table A2. Effect of essential status on likelihood of being positive for Covid-19 for all primary policyholders (1) (2) (3) (4) (5)
Essential x Post 0.00751*** (0.00011)
0.00751*** (0.00011)
0.00751*** (0.00011)
0.00751*** (0.00011)
0.00751*** (0.00011)
Essential 0.00021* (0.00009)
0.00018* (0.00009)
-0.00006 (0.00009)
-0.00001 (0.00009)
-0.00369*** (0.00011)
Age <18 -0.00454+ (0.00266)
-0.00474+ (0.00266)
-0.00454+ (0.00266)
-0.00448+ (0.00265)
Age 51-64 -0.00014* (0.00006)
0.00016** (0.00006)
0.00028*** (0.00006)
0.00017** (0.00006)
Age 65+ -0.00158*** (0.0001)
-0.00116*** (0.0001)
-0.00114*** (0.0001)
-0.00111*** (0.0001)
Female 0.00068*** (0.00005)
0.00026*** (0.00005)
0.00026*** (0.00005)
-0.00003 (0.00006)
Rural -0.00439*** (0.00027)
-0.00083** (0.00028)
-0.00015 (0.00029)
-0.00051+ (0.00029)
At least one chronic condition 0.00272***
(0.00006) 0.00263*** (0.00006)
0.00262*** (0.00006)
0.00246*** (0.00006)
At least one acute inpatient hospitalization 0.00512***
(0.00013) 0.00495*** (0.00013)
0.00493*** (0.00013)
0.00483*** (0.00013)
Member with cohabiting dependent 0.00004
(0.00006) 0.00078*** (0.00006)
0.00087*** (0.00006)
0.00073*** (0.00006)
Member with cohabiting non-dependent -0.00068***
(0.0001) -0.00038***
(0.0001) -0.00041***
(0.0001) -0.00033**
(0.0001) % Black or African American 0.01323***
(0.00017) 0.01168*** (0.00018)
0.01078*** (0.00018)
% Asian 0.01667*** (0.00054)
0.01171*** (0.00059)
0.01131*** (0.00059)
% Other race 0.02353*** (0.00198)
0.01067*** (0.00204)
0.0077*** (0.00205)
% Hispanic or Latino of any race in ZIP Code 0.002*
(0.00092) 0.00683*** (0.00095)
0.00694*** (0.00095)
% Below FPL in ZIP code -0.00494*** (0.00052)
-0.00954*** (0.00056)
-0.00909*** (0.00056)
Bucks County 0.00072*** (0.00012)
0.00066*** (0.00012)
Chester County -0.00086*** (0.00013)
-0.00037** (0.00013)
Delaware County 0.00125*** (0.00012)
0.00128*** (0.00013)
Montgomery County 0.00035** (0.00012)
0.00051*** (0.00012)
Philadelphia County 0.00286*** (0.00012)
0.00253*** (0.00013)
Administration and Support and Waste Management and Remediation Services
0.00066* (0.00033)
Agriculture, Forestry, Fishing and Hunting -0.00042
(0.00041) Arts, Entertainment, and Recreation -0.00005
(0.0006)
30
Construction 0.00009 (0.00031)
Educational Services -0.00119*** (0.00028)
Finance and Insurance -0.00079** (0.00029)
Health Care and Social Assistance 0.0065***
(0.00028)
Information -0.00125*** (0.00031)
Management of Companies and Enterprises -0.00278***
(0.00072)
Manufacturing 0.00025 (0.0003)
Mining, Quarrying, and Oil and Gas Extraction -0.00059
(0.00086) Other Services (Except Public Administration) 0.00173***
(0.00028) Professional, Scientific, and Technical Services -0.0007*
(0.00029) Real Estate and Rental and Leasing 0.00076*
(0.00034)
Retail Trade 0.00011 (0.0003)
Transportation and Warehousing 0.00723***
(0.00032)
Utilities -0.00115*** (0.00032)
Wholesale Trade -0.0004 (0.00032)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.0136 0.0136 0.0136 0.0136 0.0136 Observations 415,958 415,958 415,958 415,958 415,958
Notes. The table presents results of estimating Equation (1) using a sample of all primary policyholders. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
31
Table A3. Effect of essential status on likelihood of being positive for Covid-19 for primary policyholders less than 65 years
(1) (2) (3) (4) (5)
Essential x Post 0.0073*** (0.00012)
0.0073*** (0.00012)
0.0073*** (0.00012)
0.0073*** (0.00012)
0.0073*** (0.00012)
Essential 0.00022* (0.00009)
0.00016+ (0.00009)
-0.00008 (0.00009)
-0.00003 (0.00009)
-0.00357*** (0.00012)
Age <18 -0.00463+ (0.00268)
-0.00479+ (0.00267)
-0.00457+ (0.00267)
-0.00464+ (0.00267)
Age 51-64 -0.00016** (0.00006)
0.00015* (0.00006)
0.00029*** (0.00006)
0.00016** (0.00006)
Female 0.0007*** (0.00006)
0.00027*** (0.00006)
0.00026*** (0.00006)
0.00003 (0.00006)
Rural -0.00437*** (0.00028)
-0.0007* (0.00029)
-0.00005 (0.0003)
-0.00046 (0.0003)
At least one chronic condition 0.00282*** (0.00006)
0.00272*** (0.00006)
0.0027*** (0.00006)
0.00253*** (0.00006)
At least one acute inpatient hospitalization 0.00494***
(0.00015) 0.00469*** (0.00015)
0.00468*** (0.00015)
0.00453*** (0.00015)
Member with cohabiting dependent -0.00007
(0.00006) 0.0007*** (0.00006)
0.0008*** (0.00006)
0.00065*** (0.00006)
Member with cohabiting non-dependent -0.0006***
(0.00012) -0.0004*** (0.00012)
-0.00043*** (0.00012)
-0.00037** (0.00012)
% Black or African American 0.01365*** (0.00018)
0.01194*** (0.00019)
0.01097*** (0.00019)
% Asian 0.01656*** (0.00057)
0.01106*** (0.00062)
0.0108*** (0.00062)
% Other race 0.02772*** (0.00208)
0.01401*** (0.00214)
0.01134*** (0.00215)
% Hispanic or Latino of any race in ZIP Code 0.00097
(0.00097) 0.00606*** (0.00099)
0.00591*** (0.001)
% Below FPL in ZIP code -0.00544*** (0.00055)
-0.01077*** (0.00058)
-0.01015*** (0.00058)
Bucks County 0.00056*** (0.00013)
0.00047*** (0.00013)
Chester County -0.00097*** (0.00013)
-0.0005*** (0.00013)
Delaware County 0.00127*** (0.00013)
0.00124*** (0.00013)
Montgomery County 0.00022+ (0.00012)
0.00035** (0.00012)
Philadelphia County 0.0031*** (0.00013)
0.00271*** (0.00013)
Administration and Support and Waste Management and Remediation Services
0.0002 (0.00034)
Agriculture, Forestry, Fishing and Hunting -0.00053
(0.00043) Arts, Entertainment, and Recreation -0.0004
(0.00063)
Construction 0.00008 (0.00032)
Educational Services -0.00131*** (0.00029)
32
Finance and Insurance -0.0009** (0.0003)
Health Care and Social Assistance 0.00614***
(0.00029)
Information -0.00144*** (0.00032)
Management of Companies and Enterprises -0.0029***
(0.00073)
Manufacturing 0.0001 (0.00031)
Mining, Quarrying, and Oil and Gas Extraction -0.00057
(0.0009) Other Services (Except Public Administration) 0.00184***
(0.00029) Professional, Scientific, and Technical Services -0.00081**
(0.0003) Real Estate and Rental and Leasing 0.00015
(0.00035)
Retail Trade -0.00008 (0.00031)
Transportation and Warehousing 0.00751***
(0.00033)
Utilities -0.00104** (0.00034)
Wholesale Trade -0.00045 (0.00033)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.0137 0.0137 0.0137 0.0137 0.0137 Observations 376,220 376,220 376,220 376,220 376,220
Notes. The table presents results of estimating Equation (1) using a sample of primary policyholders less than 65 years. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
33
Table A4. Effect of essential status on likelihood of being positive for Covid-19 for primary policyholders not in the health care and social assistance subsector
(1) (2) (3) (4) (5)
Essential x Post 0.00209*** (0.00012)
0.00209*** (0.00012)
0.00209*** (0.00012)
0.00209*** (0.00012)
0.00209*** (0.00012)
Essential 0.00005 (0.00009)
0.00012 (0.00009)
0.00028** (0.00009)
0.00037*** (0.00009)
-0.00088*** (0.00011)
Age <18 -0.00398+ (0.00229)
-0.00431+ (0.00229)
-0.00418+ (0.00229)
-0.00464* (0.00229)
Age 51-64 -0.00005 (0.00006)
0.00018** (0.00006)
0.00026*** (0.00006)
0.00005 (0.00006)
Age 65+ -0.00171*** (0.00009)
-0.00136*** (0.00009)
-0.00136*** (0.00009)
-0.00145*** (0.00009)
Female -0.00033*** (0.00005)
-0.00061*** (0.00005)
-0.00061*** (0.00005) 0 (0.00005)
Rural -0.00324*** (0.00026)
-0.00052* (0.00026)
0.00013 (0.00027)
-0.0002 (0.00027)
At least one chronic condition 0.00207***
(0.00005) 0.00203*** (0.00005)
0.00202*** (0.00005)
0.00188*** (0.00005)
At least one acute inpatient hospitalization 0.00512***
(0.00012) 0.00496*** (0.00012)
0.00495*** (0.00012)
0.00489*** (0.00012)
Member with cohabiting dependent 0.00021***
(0.00005) 0.00081*** (0.00005)
0.00086*** (0.00005)
0.0007*** (0.00005)
Member with cohabiting non-dependent -0.00017+
(0.00009) 0.00009
(0.00009) 0.00006
(0.00009) 0.00002
(0.00009) % Black or African American 0.0113***
(0.00017) 0.01016*** (0.00018)
0.00926*** (0.00018)
% Asian 0.0122*** (0.00051)
0.00864*** (0.00055)
0.00891*** (0.00055)
% Other race 0.01607*** (0.00186)
0.00712*** (0.00192)
0.00415* (0.00192)
% Hispanic or Latino of any race in ZIP Code 0.00283***
(0.00085) 0.00624*** (0.00088)
0.00579*** (0.00088)
% Below FPL in ZIP code -0.00362*** (0.00051)
-0.00722*** (0.00053)
-0.00623*** (0.00053)
Bucks County 0.00065*** (0.00011)
0.00044*** (0.00011)
Chester County -0.00024* (0.00012)
-0.00009 (0.00012)
Delaware County 0.00114*** (0.00012)
0.00098*** (0.00012)
Montgomery County 0.00031** (0.00011)
0.00034** (0.00011)
Philadelphia County 0.00227*** (0.00012)
0.00184*** (0.00012)
Administration and Support and Waste Management and Remediation Services
0.00041 (0.00028)
Agriculture, Forestry, Fishing and Hunting -0.00057
(0.00035) Arts, Entertainment, and Recreation -0.00014
(0.00051)
Construction -0.00038 (0.00026)
34
Educational Services -0.00133*** (0.00024)
Finance and Insurance -0.00091*** (0.00025)
Information -0.00126*** (0.00026)
Management of Companies and Enterprises -0.00301***
(0.00061)
Manufacturing 0.00004 (0.00025)
Mining, Quarrying, and Oil and Gas Extraction -0.00102
(0.00073) Other Services (Except Public Administration) 0.00183***
(0.00024) Professional, Scientific, and Technical Services -0.00093***
(0.00025) Real Estate and Rental and Leasing 0.00067*
(0.00029)
Retail Trade -0.00025 (0.00026)
Transportation and Warehousing 0.00777***
(0.00028)
Utilities -0.00124*** (0.00027)
Wholesale Trade -0.00044 (0.00027)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.0100 0.0100 0.0100 0.0100 0.0100 Observations 341,118 341,118 341,118 341,118 341,118
Notes. The table presents results of estimating Equation (1) using a sample of primary policyholders not in the health care and social assistance subsector. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
35
Table A5. Effect of essential status on likelihood of being positive for Covid-19 for primary policyholders in the health care and social assistance subsector only
(1) (2) (3) (4)
Essential x Post 0.01241*** (0.00137)
0.01241*** (0.00137)
0.01241*** (0.00137)
0.01241*** (0.00137)
Essential -0.00004 (0.00105)
-0.00026 (0.00105)
-0.00118 (0.00105)
-0.00186+ (0.00105)
Age <18 -0.01259 (0.02582)
-0.00829 (0.0258)
-0.00795 (0.02579)
Age 51-64 -0.00008 (0.00021)
0.00045* (0.00021)
0.00076*** (0.00022)
Age 65+ 0.0004 (0.00039)
0.00096* (0.00039)
0.00119** (0.00039)
Female 0.00033 (0.00022)
0.00031 (0.00022)
0.00036 (0.00022)
Rural -0.01012*** (0.00092)
-0.00336*** (0.00094)
-0.00225* (0.00098)
At least one chronic condition 0.00513*** (0.0002)
0.00492*** (0.0002)
0.00497*** (0.0002)
At least one acute inpatient hospitalization 0.00496***
(0.00043) 0.00484*** (0.00043)
0.00483*** (0.00043)
Member with cohabiting dependent -0.00071*** (0.0002)
0.0005* (0.0002)
0.00066** (0.0002)
Member with cohabiting non-dependent -0.00175***
(0.00038) -0.00161***
(0.00038) -0.00169***
(0.00038)
% Black or African American 0.01797*** (0.00055)
0.01491*** (0.00059)
% Asian 0.03189*** (0.00184)
0.02106*** (0.00203)
% Other race 0.03298*** (0.00719)
0.00564 (0.00739)
% Hispanic or Latino of any race in ZIP Code 0.00549
(0.00348) 0.01579*** (0.00355)
% Below FPL in ZIP code -0.01156*** (0.00169)
-0.0177*** (0.0018)
Bucks County 0.00138** (0.00043)
Chester County -0.00394*** (0.0005)
Delaware County 0.00226*** (0.00046)
Montgomery County 0.00109* (0.00043)
Philadelphia County 0.00504*** (0.00043)
Industry FE No No No No Week FE Yes Yes Yes Yes Mean of DV 0.0302 0.0302 0.0302 0.0302 Observations 74,840 74,840 74,840 74,840
Notes. The table presents results of estimating Equation (1) using a sample of primary policyholders in the health care and social assistance subsector only. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, and column (4) adds county fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
36
Table A6. Effect of cohabiting with essential worker on likelihood of being positive for Covid-19 for all cohabiting dependents
(1) (2) (3) (4) (5)
Essential x Post 0.00086*** (0.00007)
0.00086*** (0.00007)
0.00086*** (0.00007)
0.00086*** (0.00007)
0.00086*** (0.00007)
Essential -0.00002 (0.00005)
0.00003 (0.00005)
-0.00008 (0.00005)
-0.00004 (0.00005)
-0.00064*** (0.00007)
Age <18 -0.00185*** (0.00004)
-0.00184*** (0.00004)
-0.00184*** (0.00004)
-0.00183*** (0.00004)
Age 51-64 0.00111*** (0.00005)
0.00123*** (0.00005)
0.00125*** (0.00005)
0.00124*** (0.00005)
Age 65+ 0.00086*** (0.00011)
0.00088*** (0.00011)
0.00088*** (0.00011)
0.00087*** (0.00011)
Female 0.0001** (0.00003)
0.00013*** (0.00003)
0.00013*** (0.00003)
0.00013*** (0.00003)
Rural -0.00148*** (0.00017)
-0.00063*** (0.00017)
-0.0002 (0.00018)
-0.00031+ (0.00018)
At least one chronic condition 0.00101*** (0.00004)
0.00101*** (0.00004)
0.00101*** (0.00004)
0.00099*** (0.00004)
At least one acute inpatient hospitalization 0.00375***
(0.0001) 0.00364***
(0.0001) 0.00361***
(0.0001) 0.00361***
(0.0001)
% Black or African American 0.00354*** (0.00012)
0.00264*** (0.00013)
0.00243*** (0.00013)
% Asian 0.00713*** (0.00035)
0.00464*** (0.00038)
0.00456*** (0.00038)
% Other race 0.0075*** (0.0013)
0.00165 (0.00134)
0.00128 (0.00134)
% Hispanic or Latino of any race in ZIP Code -0.0004
(0.00059) 0.00146* (0.00061)
0.00134* (0.00061)
% Below FPL in ZIP code 0.00266*** (0.00038)
0.00045 (0.0004)
0.00054 (0.0004)
Bucks County 0.00024** (0.00007)
0.00021** (0.00007)
Chester County 0.00008 (0.00008)
0.00017* (0.00008)
Delaware County 0.00068*** (0.00007)
0.00065*** (0.00008)
Montgomery County 0.00041*** (0.00007)
0.00041*** (0.00007)
Philadelphia County 0.00157*** (0.00008)
0.00146*** (0.00008)
Administration and Support and Waste Management and Remediation Services
0.00002 (0.00027)
Agriculture, Forestry, Fishing and Hunting 0.00078*
(0.00031) Arts, Entertainment, and Recreation 0.00178***
(0.00047)
Construction 0.00021 (0.00025)
Educational Services 0.00046* (0.00023)
Finance and Insurance 0.0004+ (0.00024)
37
Health Care and Social Assistance 0.00153*** (0.00023)
Information 0.00048* (0.00024)
Management of Companies and Enterprises -0.00022
(0.00046)
Manufacturing 0.00102*** (0.00024)
Mining, Quarrying, and Oil and Gas Extraction 0.00126*
(0.00051) Other Services (Except Public Administration) 0.00082***
(0.00023) Professional, Scientific, and Technical Services 0.00048*
(0.00024) Real Estate and Rental and Leasing 0.00093***
(0.00026)
Retail Trade 0.0004 (0.00025)
Transportation and Warehousing 0.00224*** (0.00025)
Utilities 0.0003 (0.00025)
Wholesale Trade 0.00049+ (0.00026)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.00514 0.00514 0.00514 0.00514 0.00514 Observations 387,412 387,412 387,412 387,412 387,412
Notes. The table presents results of estimating Equation (1) using a sample of all cohabiting dependents. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
38
Table A7. Effect of cohabiting with essential worker on likelihood of being positive for Covid-19 for cohabiting dependents 18 years or older
(1) (2) (3) (4) (5)
Essential x Post 0.00133*** (0.00012)
0.00133*** (0.00012)
0.00133*** (0.00012)
0.00133*** (0.00012)
0.00133*** (0.00012)
Essential -0.00001 (0.00009)
0.00006 (0.00009)
-0.0001 (0.00009)
-0.00003 (0.00009)
-0.00096*** (0.00012)
Age 51-64 0.00098*** (0.00007)
0.00116*** (0.00007)
0.00119*** (0.00007)
0.00118*** (0.00007)
Age 65+ 0.00064*** (0.00013)
0.00066*** (0.00013)
0.00065*** (0.00013)
0.00062*** (0.00014)
Female 0.00008 (0.00006)
0.00016** (0.00006)
0.00016** (0.00006)
0.00019** (0.00006)
Rural -0.0022*** (0.00027)
-0.00094*** (0.00028)
-0.0003 (0.00029)
-0.00044 (0.00029)
At least one chronic condition 0.00157*** (0.00006)
0.0016*** (0.00006)
0.0016*** (0.00006)
0.00157*** (0.00006)
At least one acute inpatient hospitalization 0.00364***
(0.00014) 0.00349*** (0.00014)
0.00344*** (0.00014)
0.00343*** (0.00014)
% Black or African American 0.00539*** (0.0002)
0.00398*** (0.00021)
0.00366*** (0.00021)
% Asian 0.01168*** (0.00058)
0.00767*** (0.00064)
0.00751*** (0.00064)
% Other race 0.0118*** (0.00216)
0.00216 (0.00223)
0.00164 (0.00223)
% Hispanic or Latino of any race in ZIP Code -0.00135
(0.00098) 0.00183+ (0.00101)
0.00173+ (0.00101)
% Below FPL in ZIP code 0.00556*** (0.00064)
0.00193** (0.00068)
0.00202** (0.00068)
Bucks County 0.00037** (0.00012)
0.00035** (0.00012)
Chester County 0.00001 (0.00012)
0.00014 (0.00013)
Delaware County 0.00108*** (0.00012)
0.00105*** (0.00013)
Montgomery County 0.00059*** (0.00012)
0.0006*** (0.00012)
Philadelphia County 0.00245*** (0.00013)
0.00232*** (0.00013)
Administration and Support and Waste Management and Remediation Services -0.00003
(0.00046) Agriculture, Forestry, Fishing and Hunting 0.00094+
(0.00054)
Arts, Entertainment, and Recreation 0.00309*** (0.00079)
Construction 0.0003 (0.00043)
Educational Services 0.0006 (0.0004)
Finance and Insurance 0.0006 (0.00041)
Health Care and Social Assistance 0.00229*** (0.00041)
39
Information 0.0006 (0.00042)
Management of Companies and Enterprises -0.00045
(0.00079)
Manufacturing 0.00141*** (0.00042)
Mining, Quarrying, and Oil and Gas Extraction 0.00214*
(0.00084) Other Services (Except Public Administration) 0.00103*
(0.0004) Professional, Scientific, and Technical Services 0.00052
(0.00041)
Real Estate and Rental and Leasing 0.00146** (0.00045)
Retail Trade 0.00066 (0.00043)
Transportation and Warehousing 0.00324*** (0.00043)
Utilities 0.00058 (0.00043)
Wholesale Trade 0.00059 (0.00044)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.0079 0.0079 0.0079 0.0079 0.0079 Observations 221,386 221,386 221,386 221,386 221,386
Notes. The table presents results of estimating Equation (1) using a sample of cohabiting dependents 18 years or older. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
40
Table A8. Effect of cohabiting with essential worker on likelihood of being positive for Covid-19 for cohabiting dependents less than 18 years
(1) (2) (3) (4) (5)
Essential x Post 0.00027*** (0.00005)
0.00027*** (0.00005)
0.00027*** (0.00005)
0.00027*** (0.00005)
0.00027*** (0.00005)
Essential -0.00003 (0.00004)
-0.00003 (0.00004)
-0.00006 (0.00004)
-0.00005 (0.00004)
-0.00021*** (0.00005)
Female 0.00005* (0.00002)
0.00005* (0.00002)
0.00005* (0.00002)
0.00005* (0.00002)
Rural -0.00038** (0.00013)
-0.00016 (0.00013)
-0.00007 (0.00013)
-0.00013 (0.00013)
At least one chronic condition 0.0001*** (0.00003)
0.00009** (0.00003)
0.00009** (0.00003)
0.00008** (0.00003)
At least one acute inpatient hospitalization 0.00396***
(0.00011) 0.00391*** (0.00011)
0.0039*** (0.00011)
0.0039*** (0.00011)
% Black or African American 0.00108*** (0.00009)
0.00093*** (0.00009)
0.00087*** (0.00009)
% Asian 0.00069** (0.00025)
0.00018 (0.00027)
0.0002 (0.00028)
% Other race 0.00168+ (0.00094)
0.00103 (0.00096)
0.001 (0.00097)
% Hispanic or Latino of any race in ZIP Code 0.00047
(0.00043) 0.00059
(0.00044) 0.00047
(0.00044)
% Below FPL in ZIP code -0.00029 (0.00027)
-0.00062* (0.00029)
-0.00056* (0.00029)
Bucks County 0.00005 (0.00005)
0.00003 (0.00005)
Chester County 0.00009 (0.00005)
0.00011+ (0.00006)
Delaware County 0.00008 (0.00005)
0.00006 (0.00006)
Montgomery County 0.00012* (0.00005)
0.00011* (0.00005)
Philadelphia County 0.00029*** (0.00006)
0.00023*** (0.00006)
Administration and Support and Waste Management and Remediation Services
0.00009 (0.00018)
Agriculture, Forestry, Fishing and Hunting 0.0006**
(0.00022)
Arts, Entertainment, and Recreation -0.00004 (0.00033)
Construction 0.00012 (0.00017)
Educational Services 0.00031+ (0.00016)
Finance and Insurance 0.00018 (0.00016)
Health Care and Social Assistance 0.00057*** (0.00016)
Information 0.00023 (0.00016)
Management of Companies and Enterprises 0.00005
(0.00032)
41
Manufacturing 0.00053** (0.00017)
Mining, Quarrying, and Oil and Gas Extraction 0.0001
(0.00038) Other Services (Except Public Administration) 0.00051**
(0.00016) Professional, Scientific, and Technical Services 0.00042*
(0.00016)
Real Estate and Rental and Leasing 0.00022 (0.00018)
Retail Trade 0.00012 (0.00017)
Transportation and Warehousing 0.00079*** (0.00017)
Utilities 0.00003 (0.00017)
Wholesale Trade 0.00046** (0.00018)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.00146 0.00146 0.00146 0.00146 0.00146 Observations 166026 166026 166026 166026 166026
Notes. The table presents results of estimating Equation (1) using a sample of cohabiting dependents less than 18 years. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
42
Table A9. Effect of cohabiting with essential worker on likelihood of being positive for Covid-19 for cohabiting non-essential non-dependents
(1) (2) (3) (4) (5)
Essential x Post 0.00349*** (0.00032)
0.00349*** (0.00032)
0.00349*** (0.00032)
0.00349*** (0.00032)
0.00349*** (0.00032)
Essential 0.00018 (0.00025)
0.00029 (0.00025)
-0.00034 (0.00025)
-0.0004 (0.00025)
-0.00039 (0.00025)
Age <18 -0.00408 (0.00299)
-0.00464 (0.00299)
-0.00458 (0.00299)
-0.00522+ (0.00299)
Age 51-64 0.00006 (0.00008)
0.00031*** (0.00008)
0.00038*** (0.00008)
0.00034*** (0.00008)
Age 65+ -0.00183*** (0.00013)
-0.00161*** (0.00013)
-0.00163*** (0.00013)
-0.00148*** (0.00013)
Female -0.0006*** (0.00007)
-0.00093*** (0.00007)
-0.00086*** (0.00007)
-0.00016* (0.00008)
Rural -0.00317*** (0.00039)
-0.00097* (0.0004)
-0.00079+ (0.00041)
-0.00132** (0.00041)
At least one chronic condition 0.00201***
(0.00008) 0.00193*** (0.00008)
0.00191*** (0.00008)
0.00185*** (0.00008)
At least one acute inpatient hospitalization 0.00519***
(0.00017) 0.00505*** (0.00017)
0.00503*** (0.00017)
0.00499*** (0.00017)
% Black or African American 0.00991***
(0.00026) 0.00864*** (0.00027)
0.00855*** (0.00027)
% Asian 0.01127*** (0.00076)
0.00784*** (0.00082)
0.00813*** (0.00082)
% Other race 0.02344*** (0.00292)
0.01445*** (0.003)
0.01166*** (0.00301)
% Hispanic or Latino of any race in ZIP Code 0.0007
(0.00134) 0.00333* (0.00138)
0.00285* (0.00138)
% Below FPL in ZIP code -0.00306***
(0.0008) -0.00833***
(0.00084) -0.00761***
(0.00085)
Bucks County -0.00002 (0.00017)
0.00008 (0.00017)
Chester County -0.00088*** (0.00017)
-0.00033+ (0.00018)
Delaware County 0.00016 (0.00017)
0.0005** (0.00017)
Montgomery County -0.00026 (0.00017)
0.00015 (0.00017)
Philadelphia County 0.00217*** (0.00018)
0.00185*** (0.00018)
Administration and Support and Waste Management and Remediation Services
0.00016 (0.00054)
Agriculture, Forestry, Fishing and Hunting -0.00268
(0.00574) Arts, Entertainment, and Recreation 0.00092
(0.00082)
Construction -0.00065 (0.0005)
Educational Services -0.00092+ (0.00048)
Finance and Insurance -0.00037 (0.00049)
43
Health Care and Social Assistance 0.00127
(0.00077)
Information -0.0029** (0.0009)
Management of Companies and Enterprises
-0.00322*** (0.00088)
Manufacturing 0.00025 (0.0005)
Mining, Quarrying, and Oil and Gas Extraction -0.00108
(0.00098) Other Services (Except Public Administration) 0.00252***
(0.00048) Professional, Scientific, and Technical Services -0.00073
(0.00049) Real Estate and Rental and Leasing 0.00143**
(0.00053)
Retail Trade -0.00016 (0.0005)
Transportation and Warehousing 0.00314+
(0.00177)
Utilities -0.00119* (0.0005)
Wholesale Trade -0.0007 (0.00071)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.00926 0.00926 0.00926 0.00926 0.00926 Observations 151377 151377 151377 151377 151377
Notes. The table presents results of estimating Equation (1) using a sample of cohabiting non-essential non-dependents. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.
44
Table A10. Effect of cohabiting with essential worker on likelihood of being positive for Covid-19 for cohabitants of the first Covid-positive member in the household
(1)
(2) (3) (4) (5)
Essential x Post 0.00453** (0.00169)
0.00453** (0.00168)
0.00453** (0.00168)
0.00453** (0.00168)
0.00453** (0.00167)
Essential -0.00014 (0.00129)
0.00046 (0.00129)
0.00033 (0.00129)
0.00072 (0.00129)
-0.00888*** (0.00171)
Age <18 -0.01894*** (0.00097)
-0.01895*** (0.00097)
-0.01897*** (0.00097)
-0.01912*** (0.00097)
Age 51-64 0.01877*** (0.00117)
0.01884*** (0.00117)
0.01894*** (0.00118)
0.01933*** (0.00118)
Age 65+ 0.01036*** (0.00176)
0.01009*** (0.00177)
0.01035*** (0.00177)
0.01027*** (0.00181)
Female 0.00243** (0.00082)
0.00236** (0.00082)
0.0023** (0.00083)
0.00242** (0.00083)
Rural 0.01148 (0.01238)
0.0126 (0.01242)
0.01776 (0.01261)
0.01441 (0.01262)
At least one chronic condition 0.01106***
(0.00089) 0.01113*** (0.00089)
0.01121*** (0.00089)
0.01114*** (0.0009)
At least one acute inpatient hospitalization 0.02816***
(0.00206) 0.02781*** (0.00206)
0.02732*** (0.00207)
0.02719*** (0.00207)
% Black or African American -0.00187
(0.00235) -0.00593* (0.00245)
-0.00756** (0.00248)
% Asian 0.04768*** (0.00823)
0.02867** (0.00876)
0.02981*** (0.00881)
% Other race 0.10396*** (0.03095)
0.072* (0.03179)
0.06123+ (0.03204)
% Hispanic or Latino of any race in ZIP Code -0.05809***
(0.01516) -0.04651** (0.0156)
-0.04437** (0.01577)
% Below FPL in ZIP code 0.02627*** (0.00781)
0.01668* (0.00815)
0.02178** (0.0082)
Bucks County 0.00169 (0.00265)
0.00337 (0.00267)
Chester County 0.00071 (0.00279)
0.00369 (0.00283)
Delaware County 0.00549* (0.0026)
0.00684** (0.00263)
Montgomery County 0.00666** (0.00254)
0.00831** (0.00257)
Philadelphia County 0.01042*** (0.00251)
0.01177*** (0.00254)
Administration and Support and Waste Management and Remediation Services
-0.02034** (0.00727)
Agriculture, Forestry, Fishing and Hunting -0.01441
(0.00887) Arts, Entertainment, and Recreation -0.03335**
(0.01144)
Construction -0.01019 (0.00733)
Educational Services -0.01813** (0.00644)
Finance and Insurance -0.01495* (0.00668)
45
Health Care and Social Assistance -0.00617
(0.00644)
Information -0.00531 (0.007)
Management of Companies and Enterprises -0.0442
(0.02838)
Manufacturing -0.00588 (0.00678)
Mining, Quarrying, and Oil and Gas Extraction 0.00579
(0.0172) Other Services (Except Public Administration) -0.01722**
(0.00642) Professional, Scientific, and Technical Services -0.02324***
(0.00674) Real Estate and Rental and Leasing -0.03055***
(0.00707)
Retail Trade -0.01901** (0.0071)
Transportation and Warehousing -0.00346
(0.00657)
Utilities -0.02573*** (0.00704)
Wholesale Trade -0.01952** (0.00735)
Week FE Yes Yes Yes Yes Yes Mean of DV 0.0739 0.0739 0.0739 0.0739 0.0739 Observations 10,117 10,117 10,117 10,117 10,117
Notes. The table presents results of estimating Equation (1) using a sample of cohabitants of the first Covid-positive member in the household. All columns include week fixed effects. Column (2) adds member-level controls, column (3) adds ZIP Code-level controls, column (4) adds county fixed effects, and column (5) adds industry fixed effects. Robust standard errors are reported in parentheses. + p<0.10, * p<0.05, ** p<0.01, *** p<0.001.